Sequence-based data such as Radio Frequency Identification (RFID) data is being deployed in application areas including supply-chain optimization, business process automation, asset tracking, and problem traceability applications. Sequence-based data reads have anomalies arising from many different sources such as duplicate reads, missed reads and cross reads. Anomalies can also occur at a logical or business process level. A small number of anomalies in RFID reads can translate into large errors in analytical results. Conventional “eager” data cleansing approaches attempt to remove all anomalies upfront, store only the cleaned data in a database, and then apply queries on the cleaned data. This attempted upfront removal of anomalies occurs, for example, during an Extract-Transform-Load (ETL) process that loads cleaned data into a data warehouse. Removal of all such anomalies upfront, however, is not always possible. One reason is that the rules and the business context required for cleansing may not be available at data loading time. For example, the presence of cycles and whether they will affect any analysis may not be known until users observe irregularity in query results some time later. As a result, an application may constantly evolve existing anomaly definitions and add new ones. Further, the rules for correcting data anomalies are often application-specific (i.e., several applications define anomalies and corrections on the same data set differently). For example, a first application queries tracking shelf space planning or labor productivity requires knowledge about all cycles within stores, whereas a second application that calculates how long a product item has stayed in every location needs to remove everything in a cycle except for the first and the last reads. Still further, for certain applications (e.g., pharmaceutical e-pedigree tracking), laws require a preservation of tracking information, thereby precluding upfront data cleansing. Moreover, when different application requirements dictate sets of rules that are dynamically changing, maintaining and adapting multiple cleaned versions is physically prohibitive. Thus, there exists a need to overcome at least one of the preceding deficiencies and limitations of the related art.